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   structures it was using.  Don't worry, I'm not going C-crazy.  I
   won't be making many (any?) more of these kinds of changes, but
   these ones were probably necessary.

 - Removed a bit of debugging code that I left in for 0.03.

0.03  Mon Sep  2 11:41:18 AEST 2002

 - Added a 'prune' parameter to new(), which controls whether the tree
   will be pruned after training.  This is usually a good idea, so the
   default is to prune.  Currently we prune using a simple
   minimum-description-length criterion.

 - Training instances are now represented using a C struct rather than
   a Perl hash.  This can dramatically reduce memory usage, though it
   doesn't have much effect on speed.  Note that Inline.pm is now
   required.

 - The list of instances is now deleted after training, since it's no
   longer needed.

lib/AI/DecisionTree.pm  view on Meta::CPAN

=over 4

=item noise_mode

Controls the behavior of the
C<train()> method when "noisy" data is encountered.  Here "noisy"
means that two or more training instances contradict each other, such
that they have identical attributes but different results.

If C<noise_mode> is set to C<fatal> (the default), the C<train()>
method will throw an exception (die).  If C<noise_mode> is set to
C<pick_best>, the most frequent result at each noisy node will be
selected.

=item prune

A boolean C<prune> parameter which specifies
whether the tree should be pruned after training.  This is usually a
good idea, so the default is to prune.  Currently we prune using a
simple minimum-description-length criterion.

=item verbose

If set to a true value, some status information will be output while
training a decision tree.  Default is false.

=item purge

If set to a true value, the C<do_purge()> method will be invoked
during C<train()>.  The default is true.

=item max_depth

Controls the maximum depth of the tree that will be created during
C<train()>.  The default is 0, which means that trees of unlimited
depth can be constructed.

=back

=item add_instance(attributes => \%hash, result => $string, name => $string)

Adds a training instance to the set of instances which will be used to
form the tree.  An C<attributes> parameter specifies a hash of
attribute-value pairs for the instance, and a C<result> parameter
specifies the result.